Rupa Patel's Ph.D. Dissertation Defense, UW Biomedical & Health Informatics
•Transferir como PPTX, PDF•
2 gostaram•2,800 visualizações
Patients with cancer experience many unanticipated symptoms and struggle to communicate them to clinicians during treatment. They contend with a variety of symptoms at home—issues stemming from cancer progression, treatment regimens, and co-morbidities. Although many patients rely on clinic visits to get help with managing these symptoms, clinicians often underestimate the intensity of patients' symptoms or miss them altogether. A proliferation of mobile and sensor-based tools, which enable self-tracking, leads us to consider how to approach their design to support cancer symptom management. However, tracking tools are not widely used and accepted in cancer care. To further study use of tracking tools, I analyzed the use of two different types of manual tracking tools: (1) ESRA-C2, an electronic Patient-Reported Outcome (ePRO) tool deployed to 372 people with cancer; and (2) HealthWeaver, a personal informatics tool deployed as a technology probe to 10 women with breast cancer. Also, I analyzed the “in-the-wild” self-tracking practices of the 10 women before they used HealthWeaver, as well as 15 other women with breast cancer. Results showed that patients who voluntarily used the ePRO tool the most frequently had relatively low symptom distress. In addition, although patients’ tracking behaviors “in the wild” were fragmented and sporadic, these behaviors with a personal informatics tool were more consistent. Participants also used tracked data to see patterns among symptoms, feel psychosocial comfort, and improve symptom communication with clinicians. Given these considerations, I describe a new conceptual model that has implications for patients, clinicians, and tool developers. If patients and clinicians accept and integrate tracking tools into cancer symptom management away from the clinic, we can move closer to continuous healing relationships that are the cornerstone of effective care.
Recomendados
Recomendados
Mais conteúdo relacionado
Mais procurados
Mais procurados (17)
Destaque
Destaque (20)
Semelhante a Rupa Patel's Ph.D. Dissertation Defense, UW Biomedical & Health Informatics
Semelhante a Rupa Patel's Ph.D. Dissertation Defense, UW Biomedical & Health Informatics (20)
Último
Último (20)
Rupa Patel's Ph.D. Dissertation Defense, UW Biomedical & Health Informatics
- 1. Designing for Use and Acceptance of Tracking Tools in Cancer Care Rupa Patel Biomedical & Health Informatics Oral Dissertation Defense August 6, 2013
- 2. Self-Tracking in Cancer Care MOTIVATION: Why Track Symptoms in Cancer Care? AIM 1: ePRO Tool Use and Symptom Distress AIM 2: Patient-Driven Self-Tracking MODEL: Design Considerations for Tracking Tools CONTRIBUTION & FUTURE WORK 2 DISSERTATIONSUMMARY
- 3. MOTIVATION: Why Track Symptoms in Cancer Care? AIM 1: ePRO Tool Use and Symptom Distress AIM 2: Patient-Driven Self-Tracking MODEL: Design Considerations for Tracking Tools CONTRIBUTION & FUTURE WORK 3 DISSERTATIONSUMMARY Self-Tracking in Cancer Care
- 5. Thumb infection Insomnia Pain Unstable glucose level Weight loss Fatigue & Nausea Hives Dry cough High blood pressure Swelling 5
- 6. Symptom Communication 6 Nausea Fatigue Pain Weight loss MOTIVATION { CLINIC VISIT }
- 7. 7 MOTIVATION Nausea Fatigue Pain Weight loss Swelling Neuropathy Symptom Communication { CLINIC VISIT }
- 8. 8 Thumb infection Vaginal dryness Dry cough Anxiety .. MOTIVATION Nausea Fatigue Pain Weight loss Swelling Neuropathy Symptom Communication { CLINIC VISIT }
- 9. • Care based on continuous healing relationships • Shared knowledge and the free flow of information • Personalization based on patient needs and values 9 Institute of Medicine (IOM) Report, 2001 MOTIVATION Communication Needs in Cancer Care
- 10. Can Tracking Tools Help? 10 MOTIVATION Cooking Hacks Website http://www.cooking-hacks.com/index.php/documentation/tutorials/ehealth- biometric-sensor-platform-arduino-raspberry-pi-medical
- 11. Researcher/Clinician-Driven • Patient-Reported Outcome • Ecological Momentary Assessment Patient-Driven • Personal Informatics Self-Tracking 11 MOTIVATION Tracking Tools Across Fields PRO EMA PInf
- 12. Abernethy, AP et al. (2008). Health Serv Res 43(6): 1975-91. Patient-Reported Outcome Instrument: FACT-G 12 MOTIVATION Cella et al, Journal of Clinical Oncology, 1993 PRO
- 13. ESRA-C2 13 MOTIVATION Symptom Assessment Report View Teaching TipsBerry, ASCO 2012. PRO
- 14. Benefits of Patient-Reported Outcome Tools • Improved health outcomes1,2 • Clinician awareness of symptoms3,4 • Timing of symptom reporting5 14 MOTIVATION 1Velikova, Journal of Clinical Oncology 2004; 2Detmar JAMA 2002 3Berry, Journal of Clinical Oncology 2011 4Ruland, JAMIA 2010 5Cleeland, Journal of Clinical Oncology 2011 PRO
- 15. Ecological MomentaryAssessment • Study of human behavior in daily life • Random or periodic reminders to track 15 Stone, Shifman, et al “The Science of Real-time Data Capture” 2007 MOTIVATION EMA
- 16. Benefits of Ecological Momentary Assessment Tools • Limited recency effects • Improved event recall • Capture of mood and context 16 MOTIVATION Stone, Shifman, et al “The Science of Real-time Data Capture” 2007 EMA
- 17. • Patient-initiated tracking • One generates data for personal insight and action 17 MOTIVATION Personal InformaticsPInf
- 18. Benefits of Personal Informatics Self-Tracking • Clinic adoption not needed • Wide selection of apps and devices • Consumer-facing interface design 18 MOTIVATION PInf
- 19. Barriers to Tracking Tool Use 19 MOTIVATION Retrospective recall X Data integration X X User burden X X X Interruptions X Interpretation of meaning X X PInfPRO EMA Donaldson, Quality of Life Research, 2008 Stone, Shifman, et al. “The Science of Real-time Data Capture,” 2007 Li, et al. CHI 2010
- 20. U.S. Symptom Tracking Habits 7 in 10 adults track a health indicator • 49% of trackers keep track “in their heads” • 34% of trackers track on paper • 21% of trackers use technology Pew Study: Tracking For Health, January 2013 Rural patients with cancer or survivors (n=134) • 1 in 3 tracked health issues during treatment • 1 in 11 used technology to track health data Hermansen-Kobulnicky et al. Support in Cancer, 2009 20 MOTIVATION
- 21. 21 How do we design tracking tools that are used and accepted by both patients and clinicians in standard cancer care? MOTIVATION
- 22. Self-Tracking in Cancer Care MOTIVATION: Why Track Symptoms in Cancer Care? AIM 1: ePRO Tool Use and Symptom Distress AIM 2: Patient-Driven Self-Tracking MODEL: Design Considerations for Tracking Tools CONTRIBUTION & FUTURE WORK 22 DISSERTATIONSUMMARY
- 23. Aim 1 Research Questions 1.1 How often do patients with cancer voluntarily use an ePRO tool? 1.2 Is frequent voluntary use of an ePRO tool associated with a reduction in symptom distress of patients with cancer? 23 AIM1:ePROTOOLUSE
- 24. Data: Intervention Group (n=372) from Randomized Controlled Trial Intervention • Voluntary access to ESRA-C2 ePRO Assessment- taking sessions at any time • Access to Teaching Tips/Report Views at Study Time Points Inclusion criteria • Any cancer • Enrollment prior to treatment start • Adults 18+ 24 AIM1:ePROTOOLUSE Berry, ASCO 2012
- 25. ESRA-C2 25 AIM1:ePROTOOLUSE Symptom Assessment Report View Teaching TipsBerry, ASCO 2012
- 26. Symptom Assessments in ESRA-C2 Questionnaires • Symptom Distress (SDS15) • Depression (PHQ-9) • Quality of Life (EORCTC-QLQ-30) • Chemotherapy-induced neuropathy (EORCTC-QLQ-CIPN30) • Skin changes • Fever/chills • Sex-related symptoms • Patient prioritization 77 total questions at study time points 30 total Symptom & Quality of Life Issues (SQLI) 26 AIM1:ePROTOOLUSE Berry, ASCO 2012
- 27. Data Collection Procedures 27 AIM1:ePROTOOLUSE Study Time Points • Symptom assessment • Reminder to take assessment • Clinician receives report Intervention: Access outside of 4 study time points • Choice of symptom assessments • Viewing reports • Viewing teaching tips Reminder phone call 1 week after enrollment at S1 Consult prior to treatment S1 First on- treatment Visit S2 6-8 weeks after treatment start S3 2-4 weeks after treatment end date S4 Berry, ASCO 2012
- 28. RQ 1.1 Analysis Methods How often do patients with cancer voluntarily use an ePRO tool? • Descriptive statistics • Contingency table / Fisher‟s Exact Test 28 AIM1:ePROTOOLUSE
- 29. Overall Use of ESRA-C2 29 AIM1:ePROTOOLUSE 0 100 200 300 400 S1 V1.1 V1.2 V1.3 V1.4 V1.5 V1.6 S2 V2.1 V2.2 V2.3 S3 V3.1 V3.2 V3.3 S4 V4.1 FrequencyofSessions S1 S2 S3 S4 Study Time Points Voluntary Sessions First on- treatment Visit S2 6-8 weeks after treatment start S3 2-4 weeks after treatment end date S4 Consult prior to treatment S1 S v Time Points over the Course of the Study
- 30. Voluntary SessionsAre Less Likely to Include Completed Symptom Distress (SDS15)Assessments Fisher‟sexact test (p < .001) 30 Frequency of Sessions with Complete SDS15 Frequency of Sessions without Complete SDS15 % of Complete SDS15 from All Sessions Study Assessment-Taking Sessions 1016 89 91.9% Voluntary Assessment-Taking Sessions 135 43 75.8% AIM1:ePROTOOLUSE S v
- 31. 31 AIM1:ePROTOOLUSE RQ 1.2 Analysis Is frequent voluntary use of an ePRO tool associated with a reduction in symptom distress of patients with cancer? One-way between-group ANOVA 31 AIM1:ePROTOOLUSE Dependent Variable • SDS15 score • Range: 15 - 60 Independent Variable • Voluntary Use • 3 levels: 0, 1, ≥2 uses
- 32. 32 AIM1:ePROTOOLUSE RQ 1.2 Analysis Is frequent voluntary use of an ePRO tool associated with a reduction in symptom distress of patients with cancer? One-way between-group ANOVA Frequent users (≥2 uses) had significantly lower end-of-study symptom distress scores than those with just 1 use (p < .05) 32 AIM1:ePROTOOLUSE Dependent Variable • SDS15 score • Range: 15 - 60 Independent Variable • Voluntary Use • 3 levels: 0, 1, ≥2 uses
- 33. Symptom Distress, by Use Group AIM1:ePROTOOLUSE 20 22 24 26 28 30 S1 S2 S3 S4 No Use (n=123) 1 Use (n=92) ≥2 Uses (n=74) SymptomDistress(SDS15Score) S1 v S2 v S3 v S4 Study Time Points Voluntary Sessions S v Time Points over the Course of the Study 33
- 34. Aim 1 Summary 34 AIM1:ePROTOOLUSE • Low overall voluntary use of ePRO tool • Frequent users had lower end-of-study symptom distress than those with 1 use • Future work to identify reasons for RCT effect
- 35. Limitations • No data on acceptability of features • Varied length of treatment • Focus on a general symptom measure 35 AIM1:ePROTOOLUSE
- 36. Self-Tracking in Cancer Care MOTIVATION: Why Track Symptoms in Cancer Care? AIM 1: ePRO Tool Use and Symptom Distress AIM 2: Patient-Driven Self-Tracking MODEL: Design Considerations for Tracking Tools CONTRIBUTION & FUTURE WORK 36 DISSERTATIONSUMMARY
- 37. Aim 2 Research Questions 2.1 What are barriers to self-tracking during cancer care? 2.2 How does actual use of tracking tools benefit patients? 37 AIM2:PATIENTTRACKING
- 38. Data Collection Methods “In-the-Wild” Field Study (n=15) • home & clinic observations • interviews • questionnaires 38 AIM2:PATIENTTRACKING “Technology Probe” Study (n=10) • tool use logs • clinic observations • interviews • questionnaires Inclusion criteria: Women with breast cancer Unruh et al, CHI 2010 Klasnja et al, CHI 2010
- 39. HealthWeaver “Check-in” Entry 39 Web Mobile AIM2:PATIENTTRACKING
- 41. Open Coding Analysis Themes • Health issues & metrics • E.g., nausea, anxiety • Tracking behavior • E.g., sporadically in notebooks • Barriers to self-tracking in the wild • Benefits of self-tracking with HealthWeaver 41 AIM2:PATIENTTRACKING
- 42. Findings: Tracking with cancer Barriers “in the Wild” • Limited clinical guidance • Fragmentation of data • Time & energy burden Benefits with HealthWeaver • Augmented memory • Psychosocial comfort • Communication support with clinicians 42 AIM2:PATIENTTRACKING Patel et al., AMIA 2012
- 43. Barrier: Limited Clinical Guidance • Patients use memory to recall symptoms • Clinicians recommend few metrics to track 43 P8‟s drain log AIM2:PATIENTTRACKING
- 44. Barrier: Fragmentation of Data • Paper, MS Office used to track • Difficult to reflect • Data unified by just 1 participant 44 P9‟s notebook AIM2:PATIENTTRACKING
- 45. HealthWeaver Tracking Usage 45 Metrics required for study Default metrics in HealthWeaver Average metrics tracked = 8.8 (n=10) AIM2:PATIENTTRACKING
- 46. Benefit: Augmenting Patterns P19: “So I was able to look back and see, I wasn’t feeling this bad, what’s going on now?” 46 AIM2:PATIENTTRACKING
- 47. Benefit: Communication Support with Clinicians P17: “I was able to show [my doctor] that my hip was getting worse over time and that she should take it a little more seriously, [given] the fact I had it for day after day after day and I could show her what was going on.” 47 AIM2:PATIENTTRACKING Patient priorities & data
- 48. Benefit: Psychosocial Comfort P23: “…[documenting] something good that happened, any new news, and good news, might be helpful to go back and remember that there have been improvements.” 48 AIM2:PATIENTTRACKING
- 49. Design Implications • Provide pre-populated metrics • Provide customizable metrics • Facilitate reflection and communication with clinicians • Support patient ownership of tracking process 49Patel et al, AMIA 2012 AIM2:PATIENTTRACKING
- 50. Aim 2 Summary • High use of personal informatics tracking tool • Unexpected benefits of self-tracking • Design implications drawn from benefits and barriers 50 AIM2:PATIENTTRACKING
- 51. Self-Tracking in Cancer Care MOTIVATION: Why Track Symptoms in Cancer Care? AIM 1: ePRO Tool Use and Symptom Distress AIM 2: Patient-Driven Self-Tracking MODEL: Design Considerations for Tracking Tools CONTRIBUTION & FUTURE WORK 51 DISSERTATIONSUMMARY
- 52. 52 How do we design tracking tools that are used and accepted by both patients and clinicians in standard cancer care? CONCEPTUALMODEL
- 53. Why are Tracking Tools Not Actually Used in Standard Cancer Care? “The approaches that are being used to develop eHealth technologies are not productive enough to create technologies that are meaningful, manageable, and sustainable.” - Julia van Gemert- Pijnen University of Twente, Netherlands 53 CONCEPTUALMODEL
- 54. Theories Informing Use and Acceptance of Tracking Tools • Technology Acceptance Model (TAM) • Derivations of TAM • Personal Informatics Stage-Based Model 54 CONCEPTUALMODEL
- 55. Technology Acceptance Model (TAM) 55 Davis 1989 CONCEPTUALMODEL
- 56. Continued Use Model CONCEPTUALMODEL 56 Kim & Malhotra 2005
- 57. Unified Theory of Acceptance and Use of Technology (UTAUT) CONCEPTUALMODEL 57 Venkatesh 2003
- 58. Issues with TAM and its Derivations • Changing facilitators affect continued use • Focuses on environment and user conditions, not technology design 58 CONCEPTUALMODEL
- 59. Stage-Based Model of Personal Informatics Systems 59 CONCEPTUALMODEL Li et al, CHI 2010
- 60. Issues with the Stage-based Model • Missing properties of tracking tools • No clinician representation 60 CONCEPTUALMODEL
- 62. TRACKING TOOL Dimensions • Modality • General vs. Condition- specific • Manual vs. Automatic • Universal vs. Personalized • Integration with EHR
- 63. TRACKING TOOL Dimensions • Structure of Data • Clinical Relevance • Completeness • Type of Vocabulary • Actual vs. Estimated • Timing of Capture • Private vs. Shared DATA
- 64. TRACKING TOOL Patient Priorities PATIENT Dimensions • Symptom Distress • Behavioral Intention • Comfort with Technology DATA
- 65. TRACKING TOOL Patient Priorities PATIENT Dimensions • Symptom Distress • Behavioral Intention • Comfort with Technology DATA CLINICIAN Dimensions • Specialization • Behavioral Intention • Comfort with Technology Clinician Priorities
- 68. TRACKING TOOL Patient Priorities DATA Clinician Priorities ACCEPTANCE ACCEPTANCE PATIENT CLINICIAN
- 69. Self-Tracking in Cancer Care MOTIVATION: Why Track Symptoms in Cancer Care? AIM 1: ePRO Tool Use and Symptom Distress AIM 2: Patient-Driven Self-Tracking MODEL: Design Considerations for Tracking Tools CONTRIBUTION & FUTURE WORK 69 DISSERTATIONSUMMARY
- 70. Contribution to Health Informatics • Uses a larger sample of voluntary ePRO tool use than prior studies • Supports convergence of multiple types of tracking tools • Considers how to integrate patient-driven tracking tools into healthcare • Introduces a new model that has implications for future tracking tool design 70 CONTRIBUTION
- 71. Contribution to Human-Computer Interaction • Provides tracking tool design considerations for people with serious illnesses like cancer 71 CONTRIBUTION
- 72. Future Work • Validate model • Interviews with patients and clinicians • Surveys • Design new tracking tools for cancer care 72 FUTUREWORK
- 73. Thank You! Committee Wanda Pratt, PhD Donna Berry, RN, PhD Paul Gorman, MD Tom Payne, MD Beth Devine, PharmD, PhD Participants in studies NIH R01 Grants NLM Informatics Fellowship 73 ACKNOWLEDGEMENTS
- 74. Pedja Klasnja Andrea Hartzler Eun Kyoung Choe Sharbani Roy Lauren Wilcox-Patterson Leila Zelnick Nadia Akhtar Rachel Hanisch Laurence Rohmer Sarah Mennicken Bas de Veer Sameer Halai Jared Bauer Alan Au Persona images: Courtesy of Limeade 74 Deepa, Alpa, Payal, Neelam Dasha & Alisher Michelle Aisha Shannon Mary Cz DUB MSR summer interns „12, „13 FHCRC Communicating for the Cure BHI-2008, BHIstudent @ UW Tito‟s asado crew Soccer friends Holdem @ home Fellow NLM fellows WISH colleagues iMed lab ACKNOWLEDGEMENTS
- 75. Questions? Rupa Patel rupatel@uw.edu Regina Holliday, Artist & Patient Advocate, Washington, DC
- 76. RQ2 Is frequent voluntary use of an ePRO tool associated with a reduction in symptom distress of patients with cancer? 76 AIM1:VOLUNTARYUSE
- 77. “Self-tracking” defined Awareness of bodily symptoms and their impact on daily activities and cognitive processes that is captured either through measurement or observations and self-report 77 RELATEDWORK
- 78. RQ2 78 AIM1:ePROTOOLUSE Frequent users‟ symptom distress was almost significantly higher in voluntary uses between T2 and T3 study time points (p < .07)
- 79. TRACKING TOOL Dimensions • Modality • General vs. Condition-specific • Manual vs. Automatic • Universal vs. Personalized • Integration with EHR Dimensions • Structure of Data • Clinical Relevance • Completeness • Type of Vocabulary • Actual vs. Estimated • Timing of Capture • Private vs. Shared Patient Priorities DATA Clinician Priorities ACCEPTANCE ACCEPTANCE PATIENT Dimensions • Symptom Distress • Behavioral Intention • Comfort with Technology CLINICIAN Dimensions • Specialization • Behavioral Intention • Comfort with Technology
Notas do Editor
- BHIDefending dissertation August 6
- UsingePRO tools – say what it is
- This is Mary, a patient with stage II breast cancer. Having cancer is quite an overwhelming experience for her.
- Her symptoms come from:the progression of the cancerside effects of chemotherapy, radiation, surgery, and/or hormone therapyconditions that have nothing to do with cancer.Mary is mostly treated on an outpatient basis, not knowing really whether something is serious and worth contacting a clinician for.
- She goes in for clinic visits periodically, and her oncologist has some common symptoms in mind to ask about.
- She remembers to tell her oncologist about her swelling and neuropathy.
- But for Mary, there’s a whole list of other things that don’t get addressed (LASER).forgets clinically relevant thumb infectionembarrassed vaginal dryness affects her quality of lifedownplays fatigueIt’s hard to remember breadth and severity of symptoms from last week.So many of Mary’s symptoms could get missed or undertreated…communication gap
- Communication between the patient and clinician is key to good quality care. The Institute of Medicine recommends that communication in the cancer care needs to be be improved in U.S. healthcare.Some specific recommendations include:…In a field where clinical interventions are studied in RCTs, it’s hard to develop tools that are proven to have an impact on communication and health outcomes.
- LASERIn a parallel universe…there are all sorts of hardware and software – that can potentially ease the burden of symptom monitoring.For example, you see a blood pressure sensor and a body temperature sensor that works on an arduino circuit board.Plus, there are many mobile apps out there that can help people track symptoms.I’m defining tracking tools to be manual or automatic, computerized or paper-based. I’m including web-based, mobile, or sensor-based tools in our definition, as well as notebooks or wall calendars.----- Meeting Notes (8/3/13 16:55) -----MOBILE APP PICTURE
- Researchers have explored using various types of tracking tools to monitor signs and symptoms.
- For example, patient-reported outcome instruments, like the one shown here, bring about clinician awareness of symptoms experienced between visits. These instruments are typically long, often validated questionnaires, as many as 80 questions or more in length.Many have the format of “how many times in the last 7 days…. have you” felt something.
- More and more, PRO tools are computerized. I’ll refer to these as “ePRO tools” – electronic patient reported outcome tools.ePRO example:ESRA-C2 developed by Donna Berry @ UW, Harvard3 main features
- They have shown benefits to symptom distress for cancer patients in randomized control trials….
- Patients are not always told exactly *when* to take PRO symptom assessments.The context of remembering a behavior or symptom is easier when the patient records it as it happens….
- Improvements on ePRO techniques is that it limits bias effects from timing of recall for example:recent events being the easiest to rememberemotionally prominent behaviors are easiest to rememberpresent mood influences what you remember from the past
- Meanwhile, Patient-Driven tools are a different kind of tracking tool altogether.Heard of QS or personal informatics? Step counter / food tracking / fitbit / scale
- Meanwhile, Patient-Driven tools are a different kind of tracking tool altogether.Heard of QS or personal informatics?
- YetPRO are designed for clinicians to consume the summaries, while patients do the work of answering as many as 80 questions. Patients don’t typically get feedback themselves, which differentiates PRO tools from self-tracking tools designed for patients for real-time data capture.REFERENCES
- While it is catching on more and more, most Americans do not track symptoms.A Pew Survey on Tracking for Health showed that : …We have found few studies on the use of tracking tools by patients with cancer.One small survey of 134 rural cancer patients and survivors showed that just 1/3 engaged in self-tracking during treatment. Fewer than 1 in 10 used a computer to track health issues. We found similar numbers in our study.We don’t know why these numbers are low or how helpful tracking was to those who did it.
- Are tracking tools used voluntarily used by patients with cancer?What unexpected benefits do patients derive from tracking tools?What are considerations for the design and acceptance of tracking tools for cancer care?
- Now I move on to a study that investigates voluntary use of an ePRO tracking tool and its relationship to factors such as symptom distress.
- The research questions for this aim are as follows:
- Secondary analysis of data collected for RCT Intervention was voluntary use of ePRO tool ESRA-C2Sites were clinics at ..Inclusion criteria..
- The ESRA-C2 ePRO tool was used in this trial for all participants.
- Assessment questions were taken from a series of validated questionnairesIndividual questions were grouped into 30 SQLIThere were 77 questions in all15 in the symptom distress scale that was the general measure we looked at
- Participants were required to take assessments for the study at home or at the clinic at 4 different time points during the treatment.These time points, were S1-S4…The Intervention provided voluntary access to ESRA-C2 at home or clinic.Participants also had the option to choose which assessments to take,RCT had an effect on symptom distress, age 50+ were more likely to use voluntarily----- Meeting Notes (8/3/13 16:55) -----S1
- Desc Stats
- Describe X, Y axisRelative time points rather than absolute time points
- Fisher’s exact testNot surprising
- PRACTICE EXPLANATION, CONTENT OF SLIDE
- Main effect of VUG
- Here you can see how symptom distress varied over timeCut scores: 26+ moderate distress, 33+ severe distress
- Comparison to WebChoice usageMore difficult to reach those with higher illness burdenNo SDS15 data from those dropping out from study----- Meeting Notes (7/31/13 11:02) -----Further study needed to understand the main effect of RCT
- Frequent users had significantly lower end-of-study symptom distress scores than those with just one useWe do not know whether participants with low symptom distress felt healthy enough to decide to use a tool on their own or whether frequent voluntary tool use helped participants lower their symptom distress.
- Patient-driven self-tracking
- To design the next generation of self-tracking tools that includes patients in the feedback loop, we addressed two major research questions.Read 1 If patients do not track during cancer treatment, why not?Read 2.
- The methods we used were mainly qualitative. We had rich data from 2 smaller studies on the information needs of 25 women with breast cancer.We conducted 3-5 interviews with participants during a 4-6 week period. For both studies, we collected photos of artifacts and tools participants used to manage information related to their cancer care.Findings from the initial study provided a basis for developing consumer health tool that we deployed in the second study.In the technology deployment, all 10 participants had access to this simple self-tracking feature. This features allowed patients capture symptoms and signs of well-being in real-time as often as they wanted.We also specifically asked technology study participants about their previous tracking behaviors in the initial interview.Technology probes let us understand the needs and desires of users in a real-world setting. We could identify behaviors and benefits that emerge from actual self-tracking tool use during cancer treatment. Many patients have not been exposed to self-tracking or found an effective way to do so.
- LASERParticipants were asked to to track at least one health metric, such as nausea, in HealthWeaver tracking. Areal-time“check-in” lets you track as many metrics as you want. To track nausea on HealthWeaver Web for example, one would drag the slider from 0 to 4. Participants could also add metrics of their own on a 0-4 scale to capture aspects of symptom severity, well-being, and pain.
- Zoom in, out on graphsIt’s important to note that tracking was just one feature of HealthWeaver, but the one that we analyzed use of in this study.
- I analyzed the two rich qualitative data sources. Iidentified health issues that participants were dealing with and noted when they tracked these issues in some tool. I also did thematic analysis in transcripts and notes using open coding.----- Meeting Notes (8/3/13 16:55) -----reference
- Barriers “in the wild” before they used HealthWeaver – make this made clearHERE’S A SUMMARY OF FINDINGS, WHICH I TALK YOU THROUGHMost of our findings of barriers to self-tracking emerged in-the-wild, when HealthWeaver was not available. Barriers were …. Meanwhile, benefits that patients talked about were drawn more often from technology deployment participants. Benefits were…
- First of all, in-the-wild, our participants monitored most health issues by memory, even though many were stressed and suffered cognitive side effects that made them feel foggy, such as “chemo brain.”They experienced many more symptoms than were covered by what clinicians suggested to track. Clinicians sometimes provided logs, like the drain log shown here, to participants after surgery to measure drain fluids and monitor the likelihood of infection. But this was not the case for other symptoms. In the wild, it was difficult to know what to track and how.
- Without access to HealthWeaver, self-tracking rarely happened.8 of 25 of all participants did create their owntracking systems with familiar tools when HealthWeaver was not part of their lives. But these systems were usually fragmented, where information about symptoms and side effects were distributed in different places.For most people, it was usually difficult to go back to and reflect on symptoms on in one place. For example, some participants took sporadic notes including reports of symptoms in notebooks and calendars that they would not always take the time to review later.One especially motivated participant did recognize fragmentation as a problem. She compiled multi-page summaries of symptoms in Microsoft Word on a weekly basis to give clinicians a better picture of her progress. She was already in the habit of doing so because she already shared summaries of her diabetes with her primary care physician.
- We were surprised by the high actual usage of HealthWeaver by the 10 participants.It is possible that most patients with cancer don’t perceive tracking to be useful or easy to do, so initial adoption does not occur.However, when we asked patients to track at least one health issue, they tracked far more than even the default issues provided by the system.Our participants customized health metrics heavily. HealthWeaver users tracked between 3-16 metrics using the tool, an average of 8.8 metrics. Only 4 were default and 1 was required.3-16What does this mean? People were customizing and adding metrics
- Also, some participants who tracked frequently took psychosocial comfort in the routine of tracking. One participant liked that she could also could look back at her own data and see she had good times during treatment. This would give her hope when the treatment worsened her side effects.----- Meeting Notes (8/3/13 16:55) -----CHANGE
- One benefit was that patients liked being able to use the tracked history graph to connect with clinicians.P17 used HealthWeaver to track her hip pain in that was bothering her for a while. She was surprised and pleased that her oncologist took the issue more seriously than before with the day by day information.The rationale for patient-reported outcome tools is that clinician awareness of symptoms can only help patients. In this case, we see that the P17 owning the data empowered her to bring up an issue that was especially bothering her.
- Also, some participants who tracked frequently took psychosocial comfort in the routine of tracking. One participant liked that she could also could look back at her own data and see she had good times during treatment. This would give her hope when the treatment worsened her side effects.----- Meeting Notes (8/3/13 16:55) -----XXXX
- These benefits allow us to step back and look at the design implications for the next generation of self-tracking tools for cancer. So many patients had little clinical guidance to start tracking on their own, but having prepopulated and customizable metrics got them started with tracking.Also, patients liked that they could reflect on the data and communicate with clinicians.Ultimately, this is because patients owned the tracking process. They used one tool. This is a departure from PRO tools, which clinicians and researchers use for their own reflection and and share this with the patient.
- ----- Meeting Notes (8/3/13 16:55) -----PERSONAL INFORMATICS
- Now I move on to a study that investigates voluntary use of an ePRO tracking tool and its relationship to factors such as symptom distress.
- Instead of repurposing beneficial tools used for clinicial trials, what would a tracking tool for patients with cancer look like?What are design considerations that help it gain acceptance by not only patients – but also clinicians?Are tracking tools used voluntarily used by patients with cancer?What unexpected benefits do patients derive from tracking tools?What are considerations for the design and acceptance of tracking tools for cancer care?
- Have a plethora of research and industry tools used to help patients track symptomsYet nothing has gained steamPRO’s been around since the 90s and earlier when it was assumed it would be part of routine cancer care.Let’s take a step back
- There are various theories in management science & HCI
- The first theory, the Technology Acceptance Model predicts voluntary initial adoption of technology.TAM is based on the theory of Reasoned Action, in which the intention to do a behavior accurately predicts whether you actually do the behavior.PU and PEOU are two constructs that influence one’s intention to use a technology like a tracking tool.
- However, tracking tools require more than one-time adoption to become beneficial to patients with cancer. TAM can be extended, as it has been in the continued use model by Kim and Malhotra.Here, the prior use at time N has an impact on later PU and PEOU, the next time you intend to use the tool, and whether you use it in the future.As an example for self-tracking, when you first use a tracking tool, you have no data on symptoms to review. So your PU of the tool could increase later on the more tracked data you have to review.
- In addition, instead of just using PU and PEOU, Ventakesh unified predictors of intention to use a technology. The UTAUT was born and became pretty accurate predictor of technology acceptance.
- Assumes a five-stage process in which one engages in self-tracking. Assumes that one takes action after reflection on data collected.Properties of the system include whether it’s user driven or system driven, or whether you are tracking one thing or multiple symptoms
- Clinician----- Meeting Notes (8/3/13 16:55) -----CHANGE
- Some tracking tool properties..COLLAPSED VERSION
- Further missing is an acknowledgement that the type and structure of the data collected is extremely important for symptom monitoring in cancer care
- The patient can drive use of the tracking tool by specifying prioritiesAnd as we saw, symptom distress can influence whether she wants to use the tool in the first place to capture symptoms
- However, we can include the clinician’s priorities as influencing what to track.Dimensions of the clinician that matter include …The orange arrows leading from both the patient and the clinician demonstrate that priorities of each are reflected in the type of data that gets collected by the tracking tool.
- The curved arrows between the patient and the clinician represent the continuous healing relationship. the clinician and patient should both foster whether a tracking tool is used or not.
- Blue for clinician, yellow for patient (now it’s red)Meanwhile, the tracking tool and the data it collects are meant to support the relationship between the clinician and patient, shown by the dashed lines leading into the continuous healing relationship. One can accept tracking, but for truly effective symptom monitoring in cancer care especially, it’s important for both to accept the tool.
- Meanwhile, the tracking tool and the data it collects are meant to support the relationship between the clinician and patient, shown by the dashed lines leading into the continuous healing relationship. One can accept tracking, but for truly effective symptom monitoring in cancer care especially, it’s important for both to accept the tool.
- Now I move on to a study that investigates voluntary use of an ePRO tracking tool and its relationship to factors such as symptom distress.
- SAD LONELY BULLET POINT
- SO MY PLAN IS..----- Meeting Notes (8/3/13 16:55) -----FIX
- 15s
- 15s
- ANOVADropped out vs. not
- In this context, I’ll define self-tracking as:Read definition.Tracking tools can be manual or automatic, computerized or paper-based. We’re including web-based, mobile, or sensor-based tools in our definition, as well as notebooks or wall calendars.
- Meanwhile, the tracking tool and the data it collects are meant to support the relationship between the clinician and patient, shown by the dashed lines leading into the continuous healing relationship. One can accept tracking, but for truly effective symptom monitoring in cancer care especially, it’s important for both to accept the tool.